Building agentic AI for SRE requires the right infrastructure since AI agents run hundreds of queries in parallel, correlate signals across logs, metrics, traces, and code changes, and compare live anomalies against historical patterns. That kind of workload demands a data layer that can keep up, and the Wild Moose and ClickHouse partnership delivers just that.
Turning Alerts into Answers in Under a Minute
Wild Moose is an agentic AI SRE platform that codifies how your best engineers debug incidents. When an alert fires, Wild Moose agents automatically gather logs, correlate signals across services, examine recent deployments, and compare against historical patterns, all in parallel. Instead of a noisy summary or a generic recommendation, it provides an evidence-backed root cause with specific next steps, delivered directly into Slack in under a minute.
What sets Wild Moose apart is that it learns your systems. It ingests your team's tribal knowledge and converts it into dynamic, self-updating playbooks that evolve with every incident. That means the platform gets smarter over time, and more engineers can resolve issues confidently, without always escalating to the same few senior experts.
A Data Backbone Built for Agentic Workloads
Agentic AI workloads are fundamentally different from human-driven queries. Where an engineer might run a handful of searches during an investigation, Wild Moose agents issue hundreds of parallel queries per alert, cross-referencing anomalies, validating hypotheses, and building a complete picture of what's happening. That requires a database that can sustain massive query concurrency while still delivering sub-second latency.
ClickHouse is purpose-built this demand with its columnar architecture, efficient compression, and rich SQL support making it capable of handling the high-throughput, low-latency queries that agentic workloads generate. Because ClickHouse stores unsampled, full-fidelity logs, Wild Moose has access to the complete historical context it needs to detect rare failure patterns and subtle correlations that sampled data would miss.
Supporting AI-driven Incident Response Under Real Production Constraints
At Wix, one of the most complex production environments in the world, this partnership powers AI-driven incident response across 4,000+ microservices, hundreds of teams, and billions of daily requests.
The Wild Moose + ClickHouse architecture delivered results within weeks: 90% root cause accuracy sustained across thousands of alerts, a projected 50% reduction in MTTR, and over 30,000 alerts enriched per month across hundreds of teams. On-call rotations became less stressful, and engineers reported feeling more confident knowing they had a system guiding them to the right answer, fast.
Read the full case study to see how Wix built AI-driven incident response at scale.
Ready to Transform Your Incident Response?
Wild Moose turns in-house expertise into actionable, production-ready AI, enriching alerts in under a minute with accuracy your engineers can rely on. Schedule a demo to see it in action.